Abstract
Efficient management of on-shelf availability and inventory is a key issue to achieve customer satisfaction and reduce the risk of profit loss for both retailers and manufacturers. Conventional store audits based on physical inspection of shelves are labor-intensive and do not provide reliable assessment. This paper describes a novel framework for automated shelf monitoring, using a consumer-grade depth sensor. The aim is to develop a low-cost embedded system for early detection of out-of-stock situations with particular regard to perishable goods stored in countertop shelves, refrigerated counters, baskets or crates. The proposed solution exploits 3D point cloud reconstruction and modelling techniques, including surface fitting and occupancy grids, to estimate product availability, based on the comparison between a reference model of the shelf and its current status. No a priori knowledge about the product type is required, while the shelf reference model is automatically learnt based on an initial training stage. The output of the system can be used to generate alerts for store managers, as well as to continuously update product availability estimates for automated stock ordering and replenishment and for e-commerce apps. Experimental tests performed in a real retail environment show that the proposed system is able to estimate the on-shelf availability percentage of different fresh products with a maximum average discrepancy with respect to the actual one of about 5.0%.
Highlights
A product is Out-Of-Stock (OOS) when it is not available on shelf for customer purchase for some contiguous time [1]
The sensor provides a 3D reconstruction of the scene, which is computed based on an infrared (IR) stereo processing algorithm running on-board the camera and is used for On-Shelf Availability (OSA) estimation, as follows
A novel framework to estimate online the on-shelf availability of products in a retail environment, based on 3D data provided by an Intel RealSense D435, is proposed
Summary
A product is Out-Of-Stock (OOS) when it is not available on shelf for customer purchase for some contiguous time [1]. Efficient Consumer Response (ECR) [2] reports that stockouts are a central concern for consumers, being the third most important issue for shoppers, after the desire for shorter lines at the cash register and more promotions. If OOS conditions occur repeatedly, customer satisfaction is reduced with potentially negative effects for both retailers and manufacturers. Significant research efforts have been devoted, so far, by academic and industrial bodies to address the OOS problem, mainly focusing on supply chain management issues. The overall OOS rate remains high in all retail sectors [3], [4], with an estimated loss of profit for the retail industry of billions of euros per year.
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